The present disclosure relates generally to systems and methods for medical imaging and, more particularly, the disclosure relates to systems and methods for automated detection and registration of medical images using advantageously correlated fiducial markers and processing algorithms.
Fiducial markers are used regularly in a wide variety of medical procedures. For example, fiducial markers are used to provide a point of reference on a patient during many surgical and therapeutic procedures, such as radiotherapy and radio surgery and medical imaging procedures.
For example, Magnetic resonance imaging (MRI) is an advantageous option as an intra-operative imaging modality for image-guided prostate interventions. While transrectal ultrasound (TRUS) is the most commonly used imaging modality to guide core needle prostate biopsy in the United States, the limited negative predictive value of the TRUS-guided systematic biopsy has been argued. To take advantage of MRI's excellent soft tissue contrast, researchers have been investigating the clinical utility of MRI for guiding targeted biopsies. MRI-guided prostate biopsies are often assisted by needle guide devices or MRI-compatible manipulators. These devices allow the radiologist to insert a biopsy needle accurately into the target defined within the MRI coordinate space.
Within this context, registering needle guide devices to the MRI coordinate system is essential for accurate needle placement. These devices are often equipped with MR-visible passive markers to be localized in the MRI coordinate system. Because the locations of those markers in the device's own coordinate system are known, one can register the device's coordinate system to the MRI coordinate system by detecting the markers on an MR image. However, the detection and registration of markers on an MR image are not always simple to achieve, because simple thresholding does not always provide robust automatic detection due to noise from other sources such as the patient's anatomy. Even if the markers are successfully detected, associating them with the individual markers is another hurdle for device-to-image registration. Existing methods rely on specific designs of fiducial frames or MR sequences, restricting the device design.
Stereotactic radiosurgery procedures often employ a physical stereotactic frame to the patient's skull to serve as a Cartesian reference. Several frames have been developed for this purpose, including the Leksell frame, Brown-Roberts-Wells (“BRW”) frame, and Fisher frame, among others. To guide the procedure, in imaging process, such as digital subtraction angiography (“DSA”) is often employed. During angiography a localizer box is attached to the frame and two-dimensional images of the patient are obtained, in which the target area for therapy can be readily identified. The two-dimensional projected target area in the DSA images is transferred to the stereotactic frame's three-dimensional coordinate system. During subsequent computed tomography (“CT”) imaging, a CT localization device is attached to the stereotactic frame, so that the obtained CT images are correlated to the stereotactic frame. During radiation treatment, the frame is attached to a stand such that the target of the therapy is accurately placed in the isocenter of the treatment system. The technique allows for precise radiation treatment; however, the use of the frame and use of the CT localization device and the need to accurately register multiple imaging and therapy modalities can be quite cumbersome.
An image-guided photon radiosurgery system, such as the CyberKnife® system manufactured by Accuray, Inc. (Sunnyvale, Calif.), is said to be a so-called “frameless” system. With a frameless, image-guided system, the invasive stereotactic frame and attached localizer box are no longer needed either during CT imaging, or radiation treatment of the patient. For brain diseases, the target treatment area can be determined on CT images, which may be fused with images obtained with other imaging modalities. To do so, imaging registration is performed using anatomical structures and fiducial markers in both images. By comparing these two-dimensional images, information regarding the translations and rotations necessary to align the two images can be determined; however, the process can be quite painstaking, as automated methods can be error prone.
For proton and heavy charged particle treatment, it is highly desirable to reduce the number of devices that intersect the treatment beam trajectory to a minimum in order to minimize unwanted attenuation of the treatment beam. In these frameless setups, for stereotactic treatment of patients, at least three small fiducials are implanted into the patient's skull, after which, positioning is guided by digitized orthogonal skull radiographs that depict the fiducials.
The frameless, image-guided approach is comfortable for the patient, and multi-fraction treatment can be routinely performed using this treatment planning approach. However, without the stereotactic frame, image registration can be very difficult and relies on anatomical markers and any fiducial markers that are employed.
Despite the fact that fiducial markers are an integral tool used to facilitate, automated methods for registering images or assisting in therapeutic planning using the fiducial markers can still be limited and error prone. It would therefore be desirable to provide a system and method for automating image analysis and image registration that does not suffer from the drawbacks described above.